TMA36 RNA Seq dataset: Indolent vs. Aggressive
Mafe Senosain
2021-01-27
Pre-processing of the data
Exploring data
Batch effect correction
Clust on Eigen GENES
## Warning: Missing column names filled in: 'X1' [1]##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## .default = col_double(),
## X1 = col_character()
## )
## ℹ Use `spec()` for the full column specifications.
Deconvolution data
XCell
CIBERSORT
quanTIseq
MCP counter
Differential gene expression analysis
## Unlist done## Labeling done## Filtering done## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors## Design done## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors## Warning: Setting row names on a tibble is deprecated.## vsd symbols done## using pre-existing size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testing## -- replacing outliers and refitting for 1732 genes
## -- DESeq argument 'minReplicatesForReplace' = 7
## -- original counts are preserved in counts(dds)## estimating dispersions## fitting model and testing## DESeq done## using 'normal' for LFC shrinkage, the Normal prior from Love et al (2014).
##
## Note that type='apeglm' and type='ashr' have shown to have less bias than type='normal'.
## See ?lfcShrink for more details on shrinkage type, and the DESeq2 vignette.
## Reference: https://doi.org/10.1093/bioinformatics/bty895## res symbols done## list doneDE results
## Pathway enrichment analysis fGSEA CANARY Good prognosis (G) is the reference. When sample is P, pathways shown below are up- or down- regulated
## `summarise()` ungrouping output (override with `.groups` argument)## Warning in fgsea(pathways = gmtPathways(pthw_path), stats = ranks, nperm
## = 1000): You are trying to run fgseaSimple. It is recommended to use
## fgseaMultilevel. To run fgseaMultilevel, you need to remove the nperm argument
## in the fgsea function call.## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (0.01% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.## Warning in fgsea(pathways = gmtPathways(pthw_path), stats = ranks, nperm
## = 1000): You are trying to run fgseaSimple. It is recommended to use
## fgseaMultilevel. To run fgseaMultilevel, you need to remove the nperm argument
## in the fgsea function call.## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (0.01% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.## Warning in fgsea(pathways = gmtPathways(pthw_path), stats = ranks, nperm
## = 1000): You are trying to run fgseaSimple. It is recommended to use
## fgseaMultilevel. To run fgseaMultilevel, you need to remove the nperm argument
## in the fgsea function call.## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (0.01% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.## Warning in fgsea(pathways = gmtPathways(pthw_path), stats = ranks, nperm
## = 1000): You are trying to run fgseaSimple. It is recommended to use
## fgseaMultilevel. To run fgseaMultilevel, you need to remove the nperm argument
## in the fgsea function call.## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (0.01% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.## Warning in fgsea(pathways = gmtPathways(pthw_path), stats = ranks, nperm
## = 1000): You are trying to run fgseaSimple. It is recommended to use
## fgseaMultilevel. To run fgseaMultilevel, you need to remove the nperm argument
## in the fgsea function call.## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (0.01% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.## Warning in fgsea(pathways = gmtPathways(pthw_path), stats = ranks, nperm
## = 1000): You are trying to run fgseaSimple. It is recommended to use
## fgseaMultilevel. To run fgseaMultilevel, you need to remove the nperm argument
## in the fgsea function call.## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (0.01% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.## Warning in fgsea(pathways = gmtPathways(pthw_path), stats = ranks, nperm
## = 1000): You are trying to run fgseaSimple. It is recommended to use
## fgseaMultilevel. To run fgseaMultilevel, you need to remove the nperm argument
## in the fgsea function call.## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (0.01% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.## Warning in fgsea(pathways = gmtPathways(pthw_path), stats = ranks, nperm
## = 1000): You are trying to run fgseaSimple. It is recommended to use
## fgseaMultilevel. To run fgseaMultilevel, you need to remove the nperm argument
## in the fgsea function call.## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (0.01% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.## Warning in fgsea(pathways = gmtPathways(pthw_path), stats = ranks, nperm
## = 1000): You are trying to run fgseaSimple. It is recommended to use
## fgseaMultilevel. To run fgseaMultilevel, you need to remove the nperm argument
## in the fgsea function call.## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (0.01% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## # A tibble: 25 x 8
## pathway pval padj ES NES nMoreExtreme size state
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <chr>
## 1 HALLMARK_G2M_CHECKPOINT 0.00157 0.00622 0.667 3.16 0 187 up
## 2 HALLMARK_E2F_TARGETS 0.00156 0.00622 0.618 2.94 0 191 up
## 3 HALLMARK_INTERFERON_GAM… 0.00154 0.00622 0.512 2.44 0 194 up
## 4 HALLMARK_ALLOGRAFT_REJE… 0.00157 0.00622 0.436 2.06 0 187 up
## 5 HALLMARK_MITOTIC_SPINDLE 0.00155 0.00622 0.421 2.00 0 195 up
## 6 HALLMARK_GLYCOLYSIS 0.00157 0.00622 0.406 1.93 0 188 up
## 7 HALLMARK_MTORC1_SIGNALI… 0.00155 0.00622 0.405 1.93 0 190 up
## 8 HALLMARK_INTERFERON_ALP… 0.00162 0.00622 0.449 1.92 0 93 up
## 9 HALLMARK_HYPOXIA 0.00159 0.00622 0.384 1.81 0 180 up
## 10 HALLMARK_IL6_JAK_STAT3_… 0.00162 0.00622 0.423 1.78 0 82 up
## # … with 15 more rows
## # A tibble: 30 x 8
## pathway pval padj ES NES nMoreExtreme size state
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <chr>
## 1 chr7p22 0.00164 0.0173 0.617 2.58 0 85 up
## 2 chr20q13 0.00152 0.0173 0.487 2.40 0 265 up
## 3 MT 0.00173 0.0173 0.702 2.39 0 31 up
## 4 chr5p15 0.00167 0.0173 0.549 2.22 0 71 up
## 5 chr14q23 0.00167 0.0173 0.530 2.18 0 77 up
## 6 chr7q22 0.00157 0.0173 0.471 2.16 0 153 up
## 7 chr1p33 0.00229 0.0173 -0.533 -2.06 0 40 down
## 8 chr14q22 0.00167 0.0173 0.518 2.04 0 62 up
## 9 chr18q12 0.00243 0.0173 -0.494 -2.02 0 51 down
## 10 chr3p22 0.00261 0.0173 -0.427 -2.00 0 118 down
## # … with 20 more rows
## # A tibble: 30 x 8
## pathway pval padj ES NES nMoreExtreme size state
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <chr>
## 1 ROSTY_CERVICAL_CANCER_PR… 0.00166 0.0215 0.821 3.69 0 130 up
## 2 FLORIO_NEOCORTEX_BASAL_R… 0.00164 0.0215 0.745 3.48 0 171 up
## 3 SOTIRIOU_BREAST_CANCER_G… 0.00163 0.0215 0.765 3.46 0 138 up
## 4 WHITEFORD_PEDIATRIC_CANC… 0.00166 0.0215 0.761 3.33 0 108 up
## 5 KOBAYASHI_EGFR_SIGNALING… 0.00159 0.0215 0.667 3.27 0 239 up
## 6 DUTERTRE_ESTRADIOL_RESPO… 0.00150 0.0215 0.645 3.26 0 305 up
## 7 SHEDDEN_LUNG_CANCER_POOR… 0.00146 0.0215 0.621 3.24 0 426 up
## 8 LEE_EARLY_T_LYMPHOCYTE_UP 0.00169 0.0215 0.758 3.24 0 95 up
## 9 CROONQUIST_IL6_DEPRIVATI… 0.00172 0.0215 0.765 3.24 0 91 up
## 10 ZHOU_CELL_CYCLE_GENES_IN… 0.00165 0.0215 0.727 3.22 0 116 up
## # … with 20 more rows
## # A tibble: 0 x 8
## # … with 8 variables: pathway <chr>, pval <dbl>, padj <dbl>, ES <dbl>,
## # NES <dbl>, nMoreExtreme <dbl>, size <int>, state <chr>
## # A tibble: 30 x 8
## pathway pval padj ES NES nMoreExtreme size state
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <chr>
## 1 GNF2_CCNA2 0.00167 0.0104 0.851 3.42 0 65 up
## 2 GNF2_CDC2 0.00167 0.0104 0.864 3.41 0 59 up
## 3 GNF2_CCNB2 0.00166 0.0104 0.872 3.38 0 54 up
## 4 GNF2_CDC20 0.00166 0.0104 0.868 3.34 0 53 up
## 5 GNF2_CENPF 0.00167 0.0104 0.842 3.32 0 58 up
## 6 GNF2_HMMR 0.00169 0.0104 0.879 3.28 0 46 up
## 7 GNF2_PCNA 0.00167 0.0104 0.816 3.28 0 65 up
## 8 GNF2_MCM4 0.00166 0.0104 0.849 3.26 0 52 up
## 9 MODULE_54 0.00150 0.0104 0.658 3.21 0 241 up
## 10 GNF2_RRM1 0.00171 0.0104 0.754 3.19 0 86 up
## # … with 20 more rows
## # A tibble: 30 x 8
## pathway pval padj ES NES nMoreExtreme size state
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <chr>
## 1 GO_DNA_DEPENDENT_DNA_REP… 0.00162 0.0408 0.606 2.78 0 138 up
## 2 GO_MITOTIC_SISTER_CHROMA… 0.00161 0.0408 0.598 2.75 0 142 up
## 3 GO_SISTER_CHROMATID_SEGR… 0.00158 0.0408 0.582 2.74 0 171 up
## 4 GO_CELL_CYCLE_DNA_REPLIC… 0.00172 0.0408 0.684 2.72 0 61 up
## 5 GO_METAPHASE_PLATE_CONGR… 0.00180 0.0408 0.699 2.71 0 54 up
## 6 GO_MITOTIC_METAPHASE_PLA… 0.00179 0.0408 0.719 2.68 0 43 up
## 7 GO_CONDENSED_CHROMOSOME_… 0.00168 0.0408 0.613 2.68 0 102 up
## 8 GO_DNA_REPLICATION 0.00151 0.0408 0.527 2.59 0 250 up
## 9 GO_CHROMOSOME_LOCALIZATI… 0.00174 0.0408 0.643 2.58 0 69 up
## 10 GO_REPLICATION_FORK 0.00176 0.0408 0.645 2.57 0 64 up
## # … with 20 more rows
## # A tibble: 30 x 8
## pathway pval padj ES NES nMoreExtreme size state
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <chr>
## 1 RB_P107_DN.V1_UP 0.00166 0.0176 0.530 2.41 0 128 up
## 2 CSR_LATE_UP.V1_UP 0.00163 0.0176 0.440 2.06 0 156 up
## 3 PRC2_EED_UP.V1_DN 0.00166 0.0176 0.429 2.03 0 179 up
## 4 ATF2_UP.V1_UP 0.00164 0.0176 0.432 2.03 0 165 up
## 5 KRAS.LUNG.BREAST_UP.V1_UP 0.00162 0.0176 0.418 1.90 0 124 up
## 6 BMI1_DN_MEL18_DN.V1_UP 0.00166 0.0176 0.413 1.89 0 135 up
## 7 PRC2_EZH2_UP.V1_DN 0.00165 0.0176 0.402 1.89 0 173 up
## 8 KRAS.300_UP.V1_DN 0.00166 0.0176 0.415 1.88 0 128 up
## 9 GCNP_SHH_UP_LATE.V1_UP 0.00164 0.0176 0.394 1.84 0 164 up
## 10 ATF2_S_UP.V1_UP 0.00164 0.0176 0.392 1.84 0 169 up
## # … with 20 more rows
## # A tibble: 30 x 8
## pathway pval padj ES NES nMoreExtreme size state
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <chr>
## 1 GSE15750_DAY6_VS_DAY10_T… 0.00156 0.0205 0.691 3.28 0 188 up
## 2 GSE15750_DAY6_VS_DAY10_E… 0.00155 0.0205 0.687 3.26 0 181 up
## 3 GSE21063_WT_VS_NFATC1_KO… 0.00154 0.0205 0.659 3.13 0 178 up
## 4 GSE24634_TEFF_VS_TCONV_D… 0.00156 0.0205 0.658 3.12 0 192 up
## 5 GSE14415_NATURAL_TREG_VS… 0.00159 0.0205 0.660 3.10 0 173 up
## 6 GSE27241_WT_VS_RORGT_KO_… 0.00162 0.0205 0.670 3.06 0 142 up
## 7 GSE39556_CD8A_DC_VS_NK_C… 0.00156 0.0205 0.640 3.03 0 185 up
## 8 GSE13547_CTRL_VS_ANTI_IG… 0.00157 0.0205 0.640 3.01 0 174 up
## 9 GSE30962_PRIMARY_VS_SECO… 0.00157 0.0205 0.628 2.97 0 184 up
## 10 GSE39110_DAY3_VS_DAY6_PO… 0.00155 0.0205 0.625 2.96 0 183 up
## # … with 20 more rows
## # A tibble: 30 x 8
## pathway pval padj ES NES nMoreExtreme size state
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <chr>
## 1 ROSTY_CERVICAL_CANCER_PR… 0.00174 0.0283 0.821 3.77 0 130 up
## 2 SOTIRIOU_BREAST_CANCER_G… 0.00174 0.0283 0.765 3.55 0 138 up
## 3 FLORIO_NEOCORTEX_BASAL_R… 0.00169 0.0283 0.745 3.53 0 171 up
## 4 GNF2_CDC2 0.00179 0.0283 0.864 3.48 0 59 up
## 5 GNF2_CCNA2 0.00181 0.0283 0.851 3.47 0 65 up
## 6 GNF2_CCNB2 0.00181 0.0283 0.872 3.42 0 54 up
## 7 GNF2_CDC20 0.00181 0.0283 0.868 3.39 0 53 up
## 8 WHITEFORD_PEDIATRIC_CANC… 0.00177 0.0283 0.761 3.39 0 108 up
## 9 GNF2_CENPF 0.00181 0.0283 0.842 3.36 0 58 up
## 10 GNF2_HMMR 0.00181 0.0283 0.879 3.34 0 46 up
## # … with 20 more rows